The Washington Post has been tracking police killings across the nation. Last week, Peter Aldhous published an analysis of these data. He figured that blacks suspects were 37.8% of all unarmed suspects killed by police. White suspects made up a nearly similar percentage of unarmed suspects killed by police, despite the fact that there are almost five times as many whites in the United States as blacks.
This does not provide the best evidence to adjudicate racial disparities in police violence, however. Aldous writes:
Video of McDonald’s last moments, shot 16 times by a white officer, made a stark contrast with images of a handcuffed Robert Lewis Dear, the white suspect in the shooting at a Planned Parenthood clinic in Colorado Springs — as activists were quick to point out.
Rather than figure out the probability that an unarmed suspect was black, it would be important to know the probability that a black suspect shotkilled by police was unarmed. We care less whether an unarmed victim was black as we do whether a black victim was unarmed. That would be more in line with, though not exactly equivalent to, what Aldhous wrote.
Below, I try to explain how we can use rules of probability to explain this problem to an introductory statistics class. Continue reading “racial disparities in police killings using bayes theorem”